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Yurii Pashchenko: Unlocking the potential of Segment Anything Model (UA)

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Yurii Pashchenko: Unlocking the potential of Segment Anything Model (UA) AI & BigData Online Day 2023 Autumn Website – www.aiconf.com.ua Youtube – https://www.youtube.com/startuplviv FB – https://www.facebook.com/aiconf

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Unlocking the potential of Segment Anything Model AI&BigData Online Day 2023
About me ❏ Yurii Pashchenko ❏ Principal Machine Learning Engineer at Depositphotos ❏ Over 10 years of research and commercial experience in applying Deep Learning models ❏ Object Detection/Segmentation and Face Recognition Specialist
Unlocking the potential of Segment Anything Model ● Image segmentation ● Segment Anything Model ● Examples of use ● Limitations
Image Segmentation
Basic Image Segmentation “Image segmentation is a sub-domain of computer vision and digital image processing which aims at grouping similar regions or segments of an image under their respective class labels” https://www.v7labs.com/blog/image-segmentation-guide#h3
Entity Segmentation http://luqi.info/Entity_Web/
Open-Vocabulary Segmentation https://paperswithcode.com/task/open-vocabulary-semantic-segmentation
SAM: Segment Anything Model
Segment Anything Model (SAM) SAM: A generalized approach to segmentation https://ai.meta.com/blog/segment-anything-foundation-model-image-segmentation/
Segment Anything Model (SAM)
SAM.Task Prompt -> Valid mask prompt can be ● a set of foreground/ background points ● rough box or mask ● free-form text, or, in general, any information indicating what to segment in an image. “valid” mask simply means that even when a prompt is ambiguous and could refer to multiple objects the output should be a reasonable mask for at least one of those objects.
SAM.Data Engine Stage Avg Annotation time,s Avg Masks/image Collected Assisted-manual stage 34->14 20->44 4.3M masks from 120k images Semi-automatic stage 14->34 44 -> 72 5.9M masks in 180k images Fully automatic stage - - 1.1 masks from 11M images
SAM.Dataset SA-1B consists of 11M diverse, high-resolution, privacy protecting images and 1.1B high-quality segmentation masks that were collected with our data engine.
SAM.Model
CLIP: Contrastive Language-Image Pre-training Learning Transferable Visual Models From Natural Language Supervision ● 400 million (image, text) pairs collected from Internet. ● Trained modifications of ResNet-50 and ViT-B ● Batch size 32 768 for 32 epochs
SAM.Model text encoder
Zero-Shot Transfer Experiments ● Single Point Valid Mask Evaluation ● Edge Detection ● Object Proposals ● Instance Segmentation ● Text-to-Mask
Examples of use
Auto-Label Data with SAM ● Encord ● v7 ● Roboflow
Matting Anything https://chrisjuniorli.github.io/project/Matting-Anything/
Matting Anything https://chrisjuniorli.github.io/project/Matting-Anything/
Inpaint Anything https://github.com/geekyutao/Inpaint-Anything
Limitations
Quality https://github.com/SysCV/sam-hq
Quality https://github.com/SysCV/sam-hq
Meta didn't release the text prompt feature for SAM Text Encoder.CLIP CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Meta didn't release the text prompt feature for SAM Text Encoder.CLIP https://github.com/xmed-lab/CLIP_Surgery/blob/master/demo.ipynb
Text Encoder.DINO https://github.com/IDEA-Research/Grounded-Segment-Anything
Semantic and Panoptic Segmentation A pipeline for panoptic segmentation can be like this: 1. Use Grounding DINO to detect the "thing" categories (categories with instances) 2. Get instance segmentation masks for the detected boxes using SAM 3. Use CLIPSeg to obtain rough segmentation masks of the "stuff" categories 4. Sample points in these rough segmentation masks and feed these to SAM to get fine segmentation masks 5. Combine the background "stuff" masks with the foreground "thing" masks to obtain a panoptic segmentation label https://github.com/segments-ai/panoptic-segment-anything
Speed.FastSAM https://github.com/CASIA-IVA-Lab/FastSAM The Fast Segment Anything Model(FastSAM) is a CNN SAM trained using only 2% of the SA-1B dataset published by SAM authors. FastSAM achieves comparable performance with the SAM method at 50× higher run-time speed.
Speed.Fast SAM https://github.com/CASIA-IVA-Lab/FastSAM
Speed.MobileSAM https://github.com/ChaoningZhang/MobileSAM
Speed.MobileSAM https://github.com/ChaoningZhang/MobileSAM
Thank you for your attention! AI&BigData Online Day 2023 Yurii Pashchenko Principal Machine Learning Engineer at Depositphotos yurii_pas george.pashchenko@gmail.com

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